Automatic medical image segmentation has made great progress owing to powerful deep
representation learning. Inspired by the success of self-attention mechanism in transformer …

Accurate segmentation of organs or lesions from medical images is crucial for reliable
diagnosis of diseases and organ morphometry. In recent years, convolutional encoder …

In the past few years, convolutional neural networks (CNNs) have achieved milestones in
medical image analysis. In particular, deep neural networks based on U-shaped architecture …

The success of Transformer in computer vision has attracted increasing attention in the
medical imaging community. Especially for medical image segmentation, many excellent …

Accurate medical image segmentation is of great significance for computer aided diagnosis.
Although methods based on convolutional neural networks (CNNs) have achieved good …

Medical image segmentation remains particularly challenging for complex and low-contrast
anatomical structures. In this paper, we introduce the U-Transformer network, which …

While CNN-based methods have been the cornerstone of medical image segmentation due
to their promising performance and robustness, they suffer from limitations in capturing long …

Abstract We propose Dual Cross-Attention (DCA), a simple yet effective attention module
that enhances skip-connections in U-Net-based architectures for medical image …

Although various methods based on convolutional neural networks have improved the
performance of biomedical image segmentation to meet the precision requirements of …

Most recent semantic segmentation methods adopt a U-Net framework with an encoder-
decoder architecture. It is still challenging for U-Net with a simple skip connection scheme to …